Heavy Metal Tolerance in Plants: Role of Transcriptomics, Proteomics, Metabolomics, and Ionomics

Genes in the ERF family encode transcriptional regulators with a variety of functions involved in the developmental and physiological processes in plants. In this study, a comprehensive computational analysis identified 122 and 139 ERF family genes in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa L. subsp. japonica), respectively. A complete overview of this gene family in Arabidopsis is presented, including the gene structures, phylogeny, chromosome locations, and conserved motifs. In addition, a comparative analysis between these genes in Arabidopsis and rice was performed. As a result of these analyses, the ERF families in Arabidopsis and rice were divided into 12 and 15 groups, respectively, and several of these groups were further divided into subgroups. Based on the observation that 11 of these groups were present in both Arabidopsis and rice, it was concluded that the major functional diversification within the ERF family predated the monocot/dicot divergence. In contrast, some groups/subgroups are species specific. We discuss the relationship between the structure and function of the ERF family proteins based on these results and published information. It was further concluded that the expansion of the ERF family in plants might have been due to chromosomal/segmental duplication and tandem duplication, as well as more ancient transposition and homing. These results will be useful for future functional analyses of the ERF family genes.

We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243-251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.